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EXPLORING THE NEW FRONTIER USING QUANTUM COMPUTING AND ARTIFICIAL INTELLIGENCE

S. Brindha, Dr. S. Ravichandran

Abstract


Artificial intelligence (AI) could be significantly impacted by quantum computing, a cutting-edge field that uses the ideas of quantum mechanics to transform computation. It examines the significant influence of quantum computing on AI techniques and applications, with an emphasis on recent developments and trends. The paper explains how quantum mechanical concepts like superposition and entanglement are changing the potential of AI systems through an examination of current studies and advancements. It explores effective AI algorithm implementations on quantum hardware, emphasizing the possibility of faster training rates and more processing power.  The study also looks at new developments in quantum-enhanced AI, such as quantum-inspired optimization methods and quantum machine learning algorithms. The study explores future possibilities and possible research avenues, imagining a world in which quantum computing is a crucial part of AI systems, enabling previously unheard-of performance levels and facilitating advances in fields like pattern recognition, optimization, and decision-making. This paper intends to guide researchers and practitioners in navigating the intricate interactions between qubits in quantum computing between quantum mechanics and artificial intelligence (AI), opening the door for revolutionary advancements in both fields and forecasting the activities of data maintenance in the future by offering insights into current trends and future possibilities.


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References


Agarwal, M. (2021). Wilson disease tissue classification and characterization using seven artificial intelligence models embedded with 3D optimization paradigm on a weak training brain magnetic resonance imaging datasets: A supercomputer application. Medical and Biological Engineering and Computing, 59(3), 511–533. https://doi.org/10.1007/s11517-021-02322-0.

Ahuja, A. S. (2023). The Digital Metaverse: Applications in Artificial Intelligence, Medical Education, and Integrative Health. Integrative Medicine Research, 12(1). https://doi.org/10.1016/j.imr.2022.100917

Almaiah, M. A. (2022). Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate. Electronics (Switzerland), 11(20). https://doi.org/10.3390/electronics11203291

M. P. Arthur, “An SVM-based multiclass IDS for multicast routing attacks in mobile ad hoc networks,” in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, Sep. 2018, pp. 363-368.

Baccour, E. (2022). Pervasive AI for IoTApplications: A Survey on Resource-Efficient Distributed Artificial Intelligence. IEEE Communications Surveys and Tutorials, 24(4), 2366–2418. https://doi.org/10.1109/COMST.2022.3200740

Buchlak, Q. D. (2021). Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review. Journal of Clinical Neuroscience, 89(Query date: 2024-11-09 23:55:55), 177–198. https://doi.org/10.1016/j.jocn.2021.04.043

Bhupesh Rawat et al. Quantum Computing and AI: Impacts & Possibilities. ADI Journal on Recent Innovation. 3(2). 2022

Cao, X. (2021). Piezoelectric Nanogenerators Derived Self-Powered Sensors for Multifunctional Applications and Artificial Intelligence. Advanced Functional Materials, 31(33). https://doi.org/10.1002/adfm.202102983.

R.N Das et al. Cryogenic Qubit Integration for Quantum Computing. 68th Electronic Components and Technology Conference. 2018. DOI 10.1109/ECTC.2018.00080.

Mingsheng Ying. Quantum computation, quantum theory and AI. Artificial Intelligence. 2020. 174.162-176.


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